A Physiological Role for Amyloid Beta Protein: Enhancement of Learning and Memory

Amyloid beta protein (A[beta]) is well recognized as having a significant role in the pathogenesis of Alzheimer's disease (AD). The reason for the presence of A[beta] and its physiological role in non-disease states is not clear. In these studies, low doses of A[beta] enhanced memory retention in two memory tasks and enhanced acetylcholine production in the hippocampus _in vivo_. We then tested whether endogenous A[beta] has a role in learning and memory in young, cognitively intact mice by blocking endogenous A[beta] in healthy 2-month-old CD-1 mice. Blocking A[beta] with antibody to A[beta] or DFFVG (which blocks A[beta] binding) or decreasing A[beta] expression with an antisense directed at the A[beta] precursor APP all resulted in impaired learning in T-maze foot-shock avoidance. Finally, A[beta]1-42 facilitated induction and maintenance of long term potentiation in hippocampal slices, whereas antibodies to A[beta] inhibited hippocampal LTP. These results indicate that in normal healthy young animals the presence of A[beta] is important for learning and memory

Relational Collaborative Filtering:Modeling Multiple Item Relations for Recommendation

Existing item-based collaborative filtering (ICF) methods leverage only the relation of collaborative similarity. Nevertheless, there exist multiple relations between items in real-world scenarios. Distinct from the collaborative similarity that implies co-interact patterns from the user perspective, these relations reveal fine-grained knowledge on items from different perspectives of meta-data, functionality, etc. However, how to incorporate multiple item relations is less explored in recommendation research. In this work, we propose Relational Collaborative Filtering (RCF), a general framework to exploit multiple relations between items in recommender system. We find that both the relation type and the relation value are crucial in inferring user preference. To this end, we develop a two-level hierarchical attention mechanism to model user preference. The first-level attention discriminates which types of relations are more important, and the second-level attention considers the specific relation values to estimate the contribution of a historical item in recommending the target item. To make the item embeddings be reflective of the relational structure between items, we further formulate a task to preserve the item relations, and jointly train it with the recommendation task of preference modeling. Empirical results on two real datasets demonstrate the strong performance of RCF. Furthermore, we also conduct qualitative analyses to show the benefits of explanations brought by the modeling of multiple item relations

Conversion Prediction Using Multi-task Conditional Attention Networks to Support the Creation of Effective Ad Creative

Accurately predicting conversions in advertisements is generally a challenging task, because such conversions do not occur frequently. In this paper, we propose a new framework to support creating high-performing ad creatives, including the accurate prediction of ad creative text conversions before delivering to the consumer. The proposed framework includes three key ideas: multi-task learning, conditional attention, and attention highlighting. Multi-task learning is an idea for improving the prediction accuracy of conversion, which predicts clicks and conversions simultaneously, to solve the difficulty of data imbalance. Furthermore, conditional attention focuses attention of each ad creative with the consideration of its genre and target gender, thus improving conversion prediction accuracy. Attention highlighting visualizes important words and/or phrases based on conditional attention. We evaluated the proposed framework with actual delivery history data (14,000 creatives displayed more than a certain number of times from Gunosy Inc.), and confirmed that these ideas improve the prediction performance of conversions, and visualize noteworthy words according to the creatives' attributes.Comment: 9 pages, 6 figures. Accepted at The 25th ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD 2019) as an applied data science pape

Geometric texture indicators for safety on AC pavements with 1mm 3D laser texture data

AbstractSurface texture and friction are two primary characteristics for pavement safety evaluation. Understanding their relationship is critical to reduce potential traffic crashes especially at wet conditions. Texture data obtained from existing systems are restricted on either a small portion on pavement surface or one line-of-sight profile, and the currently used texture indicators, such as Mean Profile Depth (MPD), and Mean Texture Depth (MTD) only reveal partial aspects of texture property. With the emerging 3D laser imaging technology, acquiring full-lane 3D pavement surface data at sub-millimeter resolution and at highway speeds has been made possible via the newly developed PaveVision3D Ultra data collection system. In this study using 1mm 3D data collected from PaveVision3D Ultra, four types of texture indicators (amplitude, spacing, hybrid, and functional parameters) are calculated to represent various texture properties for pavement friction estimation. The relationships among those texture indicators and pavement friction are examined. MPD and Skewness – two height texture parameters, Texture Aspect Ratio (TAR) – a spatial parameter, and Surface Bearing Index (SBI) – a functional parameter are found to be the four most contributing parameters for pavement friction prediction. Finally a multivariate regression model is developed based on residual plot analysis methods to estimate pavement friction with the R-squared value of 0.95. This study would be beneficial in the continuous measurement and evaluation of pavement safety for project- and network-level pavement surveys

Pavement Friction Estimation Based on the Heinrich/Klüppel Model

Tire-pavement interaction is a critical analysis for conducting friction measurements and safety evaluation on highway pavements. Substantial field studies and research efforts indicate pavement friction can be predicted with tire/texture-related models (e.g. empirical or analytical models); however, developing a reliable friction prediction model for network level pavement survey still remains a challenge. In this paper Heinrich/Klüppel friction prediction model is utilized to estimate friction on Asphalt Concrete (AC) pavements. High resolution texture data are acquired from Ames high-speed profiling system, and subsequently pavement friction data are collected on the same sections with Dynatest 6875 Highway friction tester. Findings from the study indicate a good agreement between the predicted and measured Friction Numbers (FNs). It is concluded that Heinrich/Klüppel friction theory can be used as a promising surrogate for pavement safety evaluation. This study would be beneficial for complementing the existing safety evaluation methods used in highway safety program

Transfer Learning Based Traffic Sign Recognition Using Inception-v3 Model

Traffic sign recognition is critical for advanced driver assistant system and road infrastructure survey. Traditional traffic sign recognition algorithms can't efficiently recognize traffic signs due to its limitation, yet deep learning-based technique requires huge amount of training data before its use, which is time consuming and labor intensive. In this study, transfer learning-based method is introduced for traffic sign recognition and classification, which significantly reduces the amount of training data and alleviates computation expense using Inception-v3 model. In our experiment, Belgium Traffic Sign Database is chosen and augmented by data pre-processing technique. Subsequently the layer-wise features extracted using different convolution and pooling operations are compared and analyzed. Finally transfer learning-based model is repetitively retrained several times with fine-tuning parameters at different learning rate, and excellent reliability and repeatability are observed based on statistical analysis. The results show that transfer learning model can achieve a high-level recognition performance in traffic sign recognition, which is up to 99.18 % of recognition accuracy at 0.05 learning rate (average accuracy of 99.09 %). This study would be beneficial in other traffic infrastructure recognition such as road lane marking and roadside protection facilities, and so on

Genetic and clinical assessment of 2009 pandemic influenza in southern China

Introduction: South China has a proven role in the global epidemiology of previous influenza outbreaks due to its dual seasonal pattern. We present the virologic, genetic and clinical characterization of pandemic H1N1 influenza infection (pH1N1) in Shantou and Nanchang, cities in southern China, during the second wave of the 2009-2010 pandemic. Methodology: Nasopharyngeal swabs were collected from 165 individuals with influenza-like illness (ILI) who presented to the hospitals in Shantou and Nanchang. Laboratory diagnosis and characterization was performed by real-time PCR, virus isolation in embryonated chicken eggs, and sequencing. Results: pH1N1 activity was sustained in three different temporal patterns throughout the study period. The overall positivity rate of pH1N1 was 50% with major distribution among young adults between the ages of 13 and 30 years. High fever, cough, expectoration, chest pain, myalgia, nasal discharge and efficient viral replication were observed as major clinical markers whereas a substantial number of afebrile cases (17%) was also observed. Rate of hospitalization and disease severity (39%) and recovery (100%) were also high within the region. Furthermore, severe complications were likely to develop in young adults upon pH1N1 infection. Genetic characterization of the HA and NA genes of pH1N1 strains exhibited homogenous spread of pH1N1 strains with 99% identity with prototypic strains; however, minor unique mutations were also observed in the HA gene. Conclusion: The study illustrates the detailed characteristics of 2009 influenza pandemic in southern parts of China that might help to strategize preparedness for future pandemics and subsequent influenza seasons.</br

Re-examining the leader-follower relationship and supportive leadership: The role of the leader as attachment figure

Ph.DDOCTOR OF PHILOSOPH

Kebudayaan Hibrid Dalam Alam Bina Malaysia: Kajian Kes Masyarakat Baba Nyonya Melaka

Tesis ini mengkaji tentang pengaruh 'kehibridan' atau ciri-ciri hibrid dalam sesebuah budaya hibrid ke atas budaya dan alam binanya. Budaya hibrid Baba Nyonya di pilih sebagai kajian kes. Bab 2 akan membincangkan ciri-ciri hibrid dalam aspek fizikal alam bina masyarakat Baba Nyonya. Seterusnya, bab 3 akan membincangkan ciri-ciri hibrid dalam aspek sosio ekonomi. Budaya dan alam bina Baba Nyonya pada masa kini semakin merosot berbanding dengan zaman penjajahan British. Oleh itu tesis ini akan mengkaji bagaimana ciri-ciri hibrid dalam aspek sosio ekonomi Baba Nyonya mengakibatkan budaya uniknya semakin merosot seperti yang dihuraikan dalam bab 4